A stochastic neighborhood conditional autoregressive model for spatial data
نویسندگان
چکیده
منابع مشابه
Proper multivariate conditional autoregressive models for spatial data analysis.
In the past decade conditional autoregressive modelling specifications have found considerable application for the analysis of spatial data. Nearly all of this work is done in the univariate case and employs an improper specification. Our contribution here is to move to multivariate conditional autoregressive models and to provide rich, flexible classes which yield proper distributions. Our app...
متن کاملA Bayesian conditional autoregressive geometric process model for range data
Extreme value theories indicate that the range is an efficient estimator of local volatility on a financial asset return. This paper proposes a novel geometric process (GP) framework for range data that nests the well known Conditional Autoregressive Range (CARR) model. We extend the GP model of Lam (1988) to a Conditional Autoregressive Geometric Process Range (CARGPR) model that allows for fl...
متن کاملConditional Maximum Likelihood Estimation of the First-Order Spatial Integer-Valued Autoregressive (SINAR(1,1)) Model
‎Recently a first-order Spatial Integer-valued Autoregressive‎ ‎SINAR(1,1) model was introduced to model spatial data that comes‎ ‎in counts citep{ghodsi2012}‎. ‎Some properties of this model‎ ‎have been established and the Yule-Walker estimator has been‎ ‎proposed for this model‎. ‎In this paper‎, ‎we introduce the...
متن کاملAutoregressive conditional root model
In this paper we develop a time series model which allows long-term disequilibriums to have epochs of non-stationarity, giving the impression that long term relationships between economic variables have temporarily broken down, before they endogenously collapse back towards their long term relationship. This autoregressive root model is shown to be ergodic and covariance stationary under some r...
متن کاملModelling Time Series Count Data: An Autoregressive Conditional Poisson Model
This paper introduces and evaluates new models for time series count data. The Autoregressive Conditional Poisson model (ACP) makes it possible to deal with issues of discreteness, overdispersion (variance greater than the mean) and serial correlation. A fully parametric approach is taken and a marginal distribution for the counts is specified, where conditional on past observations the mean is...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2009
ISSN: 0167-9473
DOI: 10.1016/j.csda.2008.08.010